Embeddings
Embedding models map text to vectors in a high-dimensional space. These vectors can be used for a variety of tasks, such as semantic similarity, clustering, and classification.
Body
Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less. Example Python code for counting tokens.
The model to use for generating the embeddings. The model must be one of the following: text-embedding-ada-002, text-embedding-3-small, or text-embedding-3-large.
The format to return the embeddings in. Can be either float or base64.
float, base64 The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Response
The list of embeddings generated by the model.
The name of the model used to generate the embedding.
The object type, which is always "list".
list The usage information for the request.